scholarly journals Crack Identification Algorithm FY 15 DE07 SW C2 Zone 3 9 Images: Y13:15 X9:11

2020 ◽  
Author(s):  
James Wendelberger ◽  
Elizabeth Kelly ◽  
Kimberly Kaufeld ◽  
John Berg
Keyword(s):  
Crack Identification ◽  
Identification Algorithm

scholarly journals DAMAGE DIAGNOSIS IN BEAM-LIKE STRUCTURES BY ARTIFICIAL NEURAL NETWORKS

2015 ◽  
Vol 21 (5) ◽  
pp. 591-604 ◽  
Author(s):  
Kamil Aydin ◽  
Ozgur Kisi
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Natural Frequencies ◽  
Network Models ◽  
Crack Identification ◽  
Identification Algorithm ◽  
Radial Basis Neural Networks ◽  
The Neural Networks ◽  
Damaged Beams ◽  
Artificial Neural

Applicability of artificial neural networks is examined in determining the natural frequencies of intact beams and crack parameters of damaged beams. Multi-layer perceptron (MLP) and radial basis neural networks (RBNN) are utilized for training and validation of input data. In the first part of the study, the first four frequencies of free vibration are predicted based on beam properties by the networks. Showing the effectiveness of the neural networks in predicting the vibrational frequencies, the second part of the study is carried out. At this stage of the inverse problem, the frequencies and mode shape rotation deviations in addition to beam properties are used as input to the networks to determine the crack parameters. Different hidden nodes, epochs and spread values are tried to find the optimal neural networks that give the lowest error estimates. In both parts of the study, the RBNN model performs better. The robustness of the network models in the presence of noise is also shown. It is shown that the optimal MLP network predicts the crack parameters slightly better in the presence of noise. As a conclusion, the trained RBNN model can be used in health monitoring of beam-like structures as a crack identification algorithm.

scholarly journals Crack Detection and Evolution Law for Rock Mass under SHPB Impact Tests

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Xie Beijing ◽  
Dihao Ai ◽  
Yu Yang
Keyword(s):  
Rock Mass ◽  
Crack Detection ◽  
Learning Algorithm ◽  
Failure Process ◽  
Quantitative Result ◽  
Crack Identification ◽  
Rock Surface ◽  
Identification Algorithm ◽  
Evolution Law ◽  
Damage Process

In order to accurately identify and quantitatively calculate the surface cracks of rock mass under SHPB impact loading, an automatic crack detection algorithm was proposed and evaluated by the experiment. In SHPB experiment, cracks on the rock surface can effectively reflect its current state and better analyze the damage process. Firstly, the SHPB system was used to impact 12 groups of rock specimens under different impact velocities. A high-frame camera with 50,000 FPS was used to capture the damage process of the rock mass; using the manual annotation method, we got a dataset of SHPB damage images including a total of 310 original images and 310 corresponding cracked annotations. Secondly, a deep convolution network model named CrackSHPB was designed based on a deep learning algorithm. The algorithm can automatically identify the crack on the rock surface during impact damage process and further provide a quantitative result of cracks, crack area. Finally, after the crack on the rock surface in each frame image was identified automatically through the model, cracks were quantitatively analyzed by the proposed algorithm, the growth rate of cracks was calculated, and their evolution law was concluded. The crack identification algorithm proposed in this paper can provide a more accurate quantitative method for rock damage by cracks on the rock surface, and evolution law can further explain the failure process of rock at high strain rate.

Model-Based Switching-Crack Identification in a Jeffcott Rotor With an Offset Disk Integrated With an Active Magnetic Bearing

2016 ◽  
Vol 138 (3) ◽  
Author(s):  
Sandeep Singh ◽  
Rajiv Tiwari
Keyword(s):  
Degrees Of Freedom ◽  
Equations Of Motion ◽  
Vibration Signal ◽  
Magnetic Bearing ◽  
Active Magnetic Bearing ◽  
Crack Identification ◽  
Identification Algorithm ◽  
Full Spectrum ◽  
Jeffcott Rotor ◽  
Domain Transformation

Vibration characteristics of a cracked Jeffcott rotor with an offset disk under the action of an active magnetic bearing (AMB), implemented to improve the radial positioning of the rotor, has been studied. Presence of the AMB suppresses the vibration induced due to the crack and unbalance; identification of the crack could be made by utilizing the vibration signal in conjunction with the controller current of the AMB. A four degrees-of-freedom (DOF) cracked rotor is modeled considering the gyroscopic effect due to the offset disk and a switching crack excitation function (SCEF) to introduce the breathing of crack. The dynamic condensation is applied to eliminate rotational displacements, which would pose practical difficulty in accurate measurement, from the system equations of motion (EOM) to develop an identification algorithm. Frequency domain transformation of the EOM is made with the help of the full spectrum analysis. An algorithm developed with the purpose of crack identification in the form of additive crack stiffness estimates the viscous damping, disk unbalance, and AMB constants as well. The algorithm has been tested for the measurement noise (in the displacement and the current) and bias errors in system parameters, and found to be robust.

Model-Based Crack Identification Using Full-Spectrum

2013 ◽  
Author(s):  
Shravankumar Chandrasekaran ◽  
Rajiv Tiwari
Keyword(s):  
Equations Of Motion ◽  
Crack Identification ◽  
Force Model ◽  
Identification Algorithm ◽  
Full Spectrum ◽  
Rotor Systems ◽  
Model Based ◽  
Fault Parameters ◽  
Harmonic Components ◽  
Rotor Model

This paper illustrates the application of full-spectrum technique for model-based identification of the crack and unbalance multi-fault parameters in cracked rotor systems. The rotor model chosen is a Laval rotor with disc unbalance and transverse surface crack. The crack force model is a switching crack, which has harmonic components exciting the rotor both in the same and reverse directions of the rotor spin. Development of identification algorithm uses linearized equations of motion in frequency domain. Full-spectrum obtains the complex Fourier coefficients of the force as well as the response. Further usage of these coefficients in the identification algorithm estimates the viscous damping, disc eccentricity, and additive crack stiffness as fault parameters. The accuracy of estimates increases on considering measurements at multiple spin speeds. The algorithm tests reasonably robust for various levels of measurement noise and bias errors in system parameters.

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